150 lines
3.2 KiB
Plaintext
150 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 535,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 536,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-0/in.tsv', 'r') as f:\n",
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" dev_x = f.readlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 538,
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"metadata": {},
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"outputs": [],
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"source": [
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"m_vocabulary = ['komputer', 'komputerze', 'aucie', 'auto', 'samochód', 'samochodzie', 'piwie', 'piwo', 'alkoholu', 'alkohol', 'żonie', 'żona', 'xboxie', 'xbox', 'co', 'e', 'XD', 'stary', 'staremu']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 539,
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"metadata": {},
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"outputs": [],
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"source": [
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"f_vocabulary = ['zakupy', 'zakupach', 'mężem', 'mąż', 'nasze', 'my', 'dzieckiem', 'dziecko', 'domu', 'dom', 'mieszkaniu', 'mieszkanie', 'kocham', 'kocha', 'chłopakowai', 'chłopak', 'haha', 'boże', 'uh', 'uhh', \":)\", 'mama', 'mamie', 'włosy']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 540,
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict(text):\n",
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" score = 0\n",
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"\n",
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" for word in m_vocabulary:\n",
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" if word in text:\n",
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" score += 1\n",
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"\n",
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" for word in f_vocabulary:\n",
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" if word in text:\n",
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" score -= 1\n",
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"\n",
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" if score == 0:\n",
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" return random.randint(0, 1)\n",
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" if score >0:\n",
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" return 1\n",
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" if score <0:\n",
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" return 0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 541,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-0/expected.tsv', 'r') as f:\n",
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" dev_y = f.readlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 542,
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"metadata": {},
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"outputs": [],
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"source": [
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"dev = zip(dev_x, dev_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 543,
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"metadata": {},
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"outputs": [],
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"source": [
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"correct = 0\n",
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"incorrect = 0\n",
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"\n",
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"with open('dev-0/out.tsv', 'wt') as f:\n",
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" for x, y in list(dev):\n",
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" f.write(str(predict(x))+'\\n')\n",
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" if predict(x) == int(y):\n",
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" correct += 1\n",
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" else:\n",
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" incorrect += 1\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 544,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.5122792230182751"
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]
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},
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"execution_count": 544,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"correct/(correct+incorrect)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "8a24ca87d97ac268fc796e79e77f73ca37fd3e060a17758a6f2d8f8d4f13ae6a"
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},
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"kernelspec": {
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"display_name": "Python 3.9.7 ('base')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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